Correlation matrix
ann_colors=list(c("red", "green","blue")[komponenty])
ann=ann_colors[[1]]
kor=cor(all_election_2, use="pairwise.complete.obs")
pheatmap(kor, display_numbers = T, annotation_colors = ann)
PCA
pca <- prcomp(all_election_bezbledow, center = TRUE, scale. = TRUE)
varimax4 <- varimax(pca$rotation[,1:3])
varimax4$loadings
##
## Loadings:
## PC1 PC2 PC3
## population_size -0.221
## forest_density 0.248 -0.140
## longitude 0.468
## PIS_support 0.546
## income -0.144 -0.162
## postproduction_age 0.251
## internal_emigration 0.302
## external_emigration -0.167 -0.142
## internal_immigration 0.301
## external_immigration 0.301
## industry_revenue 0.285
## empl_agriculture 0.526
## empl_industry 0.233 -0.113
## empl_services 0.315
## poulation_density -0.263 -0.147
## arrival_SARS 0.270 -0.123
## size_COVID 0.130 -0.179
## mob_in 0.281
## mob_out -0.348
## mobility 0.248 -0.114
## log_mobility -0.382
## pagerank_mob 0.300
## betweenness_mob 0.146 -0.150
## closeness_mob -0.252
## senior_fraction 0.211 0.155
## industry_fraction 0.252
## neighbors_size -0.115 -0.378
## neighbors_arr -0.186
## PM -0.274 0.259
##
## PC1 PC2 PC3
## SS loadings 1.000 1.000 1.000
## Proportion Var 0.034 0.034 0.034
## Cumulative Var 0.034 0.069 0.103
all_pca=summary(pca)
all_pca
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 3.5960 1.66980 1.50897 1.20151 1.07072 1.01375
## Proportion of Variance 0.4459 0.09615 0.07852 0.04978 0.03953 0.03544
## Cumulative Proportion 0.4459 0.54206 0.62058 0.67036 0.70989 0.74533
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.96707 0.9633 0.84831 0.82924 0.81837 0.79415
## Proportion of Variance 0.03225 0.0320 0.02481 0.02371 0.02309 0.02175
## Cumulative Proportion 0.77758 0.8096 0.83439 0.85810 0.88119 0.90294
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 0.69472 0.65449 0.63049 0.56435 0.53630 0.44488
## Proportion of Variance 0.01664 0.01477 0.01371 0.01098 0.00992 0.00682
## Cumulative Proportion 0.91959 0.93436 0.94806 0.95905 0.96896 0.97579
## PC19 PC20 PC21 PC22 PC23 PC24
## Standard deviation 0.43328 0.38259 0.32178 0.27864 0.27123 0.25460
## Proportion of Variance 0.00647 0.00505 0.00357 0.00268 0.00254 0.00224
## Cumulative Proportion 0.98226 0.98731 0.99088 0.99356 0.99609 0.99833
## PC25 PC26 PC27 PC28 PC29
## Standard deviation 0.14915 0.12292 0.08283 0.06516 5.702e-16
## Proportion of Variance 0.00077 0.00052 0.00024 0.00015 0.000e+00
## Cumulative Proportion 0.99910 0.99962 0.99985 1.00000 1.000e+00
Full reggression model
#spa vs no
arr=all_election_2[,-c(20)]
arr2=all_election_bezbledow[,-c(20,25,27,28,17)]
arr2_=all_election_2[,-c(20,25,27,28,17)]
#spatial
#arr$neighbors_arr=all_election$neighbors_arr
size=all_election_2[,-c(20)]
size2=all_election_bezbledow[,-c(20,25,27,28,16)]
size2_=all_election_2[,-c(20,25,27,28,16)]
#spatial
#size$PM=as.numeric(paste(size$PM))
mod_size=lm(size_COVID~., data=size)
mod_size2=lm(size_COVID~., data=size2)
mod_size2_=lm(size_COVID~., data=size2_)
af <- anova(mod_size)
afss <- af$"Sum Sq"
anova(mod_size)
proc<-cbind(af,PctExp=afss/sum(afss)*100)
et=colnames(size)[-17]
#et=colnames(size)[-16]
#et[24]="Residuals"
proc=proc[-28,]
#proc=proc[-24,]
komponenty1=komponenty[-c(17,20)]
pdf("size_full_.pdf")
plot(proc$PctExp, xaxt = "n", main="%explained variance: No. cases (full model)", xlab="", ylab = "%",col = c("red", "green","blue")[komponenty1], pch = c(16, 17, 18)[komponenty1])
axis(1, at=1:27, labels=FALSE)
text(seq(1, 27, by=1), par("usr")[3]-0.6, par("usr")[2]+0, 0, cex=0.57, labels = et, srt = 90, pos = 2, xpd = TRUE)
dev.off()
## png
## 2
mod_arr=lm(arrival_SARS~., data=arr)
mod_arr2=lm(arrival_SARS~., data=arr2)
mod_arr2_=lm(arrival_SARS~., data=arr2_)
af <- anova(mod_arr)
afss <- af$"Sum Sq"
proc<-cbind(af,PctExp=afss/sum(afss)*100)
et=colnames(arr)[-16]
komponenty1=komponenty[-c(16,20)]
#et[24]="Residuals"
#krotsze
#proc=proc[-24,]
proc=proc[-28,]
#plot(proc$PctExp, main="%wyjasnienia zmiennsci czasu_dojscoa w modelu liniowym")
pdf("arr_full_.pdf")
plot(proc$PctExp, xaxt = "n", ylim=c(0,14), main="%explained variance: arrival time (full model)", xlab="", ylab="%", col = c("red", "green","blue")[komponenty1], pch = c(16, 17, 18)[komponenty1])
axis(1, at=1:27, labels=FALSE)
text(seq(1, 27, by=1), par("usr")[3] -0.6, par("usr")[2]+0,0, cex=0.6, labels = et, srt = 90, pos = 2, xpd = TRUE)
#legend(1,5, legend=c("a"))
legend(1, 14, legend=c("demographic cluster", "covid-like cluster", "social cluster"), col = c("red", "green","blue"), pch = c(16, 17, 18), cex=0.8)
dev.off()
## png
## 2
#mod_arr=lm(arrival_SARS~., data=arr)
#summary(mod_arr)
step.model <- stepAIC(mod_arr, direction = "both", trace = FALSE)
step.model2 <- stepAIC(mod_arr2, direction = "both", trace = FALSE)
step.model2_ <- stepAIC(mod_arr2_, direction = "both", trace = FALSE)
#mod_size=lm(size_COVID~., data=size)
step.model_s <- stepAIC(mod_size, direction = "both", trace = FALSE)
step.model_s2 <- stepAIC(mod_size2, direction = "both", trace = FALSE)
step.model_s2_ <- stepAIC(mod_size2_, direction = "both", trace = FALSE)
mod=tab_model(step.model, step.model_s, digits = 2)
mod
| Â | arrival_SARS | size_COVID | ||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 97.43 | 72.48 – 122.37 | <0.001 | -57.32 | -97.05 – -17.60 | 0.005 |
| forest_density | 7.83 | -2.70 – 18.36 | 0.144 | |||
| longitude | 0.56 | -0.21 – 1.32 | 0.151 | |||
| PIS_support | -0.18 | -0.37 – 0.00 | 0.052 | |||
| postproduction_age | -0.00 | -0.00 – 0.00 | 0.095 | |||
| internal_emigration | 0.00 | -0.00 – 0.01 | 0.082 | |||
| external_emigration | -0.01 | -0.02 – 0.00 | 0.123 | 0.11 | 0.05 – 0.17 | <0.001 |
| internal_immigration | -0.00 | -0.00 – -0.00 | 0.039 | |||
| empl_agriculture | -0.00 | -0.00 – 0.00 | 0.108 | |||
| size_COVID | -0.01 | -0.03 – 0.00 | 0.115 | |||
| mob_in | 0.00 | -0.00 – 0.00 | 0.061 | |||
| mob_out | 0.00 | 0.00 – 0.00 | 0.027 | 0.00 | 0.00 – 0.01 | 0.003 |
| log_mobility | -8.57 | -10.99 – -6.15 | <0.001 | |||
| industry_revenue | -0.00 | -0.01 – -0.00 | 0.030 | |||
| empl_services | 0.00 | 0.00 – 0.00 | <0.001 | |||
| poulation_density | -0.02 | -0.03 – -0.00 | 0.028 | |||
| arrival_SARS | -0.44 | -0.94 – 0.06 | 0.081 | |||
| pagerank_mob | -2439.35 | -5597.98 – 719.28 | 0.130 | |||
| neighbors_size | 0.00 | 0.00 – 0.00 | <0.001 | |||
| PM | 1.94 | 0.92 – 2.95 | <0.001 | |||
| industry_fraction | 0.00 | -0.00 – 0.00 | 0.064 | |||
| Observations | 380 | 380 | ||||
| R2 / R2 adjusted | 0.284 / 0.260 | 0.529 / 0.516 | ||||
mod2=tab_model(step.model2, step.model_s2, digits = 4, show.ci = FALSE)
mod2
| Â | arrival_SARS | size_COVID | ||
|---|---|---|---|---|
| Predictors | Estimates | p | Estimates | p |
| (Intercept) | 103.4721 | <0.001 | -99.0446 | <0.001 |
| longitude | 0.6502 | 0.096 | ||
| PIS_support | -0.2037 | 0.033 | ||
| internal_emigration | 0.0030 | 0.049 | ||
| external_emigration | -0.0123 | 0.044 | 0.1402 | <0.001 |
| internal_immigration | -0.0021 | 0.085 | ||
| empl_agriculture | -0.0004 | 0.032 | ||
| mob_out | 0.0004 | 0.119 | 0.0042 | <0.001 |
| log_mobility | -7.6686 | <0.001 | ||
| closeness_mob | -258546.5005 | 0.088 | ||
| industry_revenue | -0.0030 | 0.018 | ||
| empl_services | 0.0012 | <0.001 | ||
| industry_fraction | 0.0001 | 0.023 | ||
| PM | 2.7087 | <0.001 | ||
| Observations | 370 | 370 | ||
| R2 / R2 adjusted | 0.273 / 0.255 | 0.514 / 0.505 | ||
mod_all=tab_model(step.model,step.model2, step.model_s,step.model_s2, digits = 4, show.ci = FALSE, show.aic=TRUE)
mod_all
| Â | arrival_SARS | arrival_SARS | size_COVID | size_COVID | ||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | p | Estimates | p | Estimates | p | Estimates | p |
| (Intercept) | 97.4251 | <0.001 | 103.4721 | <0.001 | -57.3222 | 0.005 | -99.0446 | <0.001 |
| forest_density | 7.8301 | 0.144 | ||||||
| longitude | 0.5588 | 0.151 | 0.6502 | 0.096 | ||||
| PIS_support | -0.1842 | 0.052 | -0.2037 | 0.033 | ||||
| postproduction_age | -0.0001 | 0.095 | ||||||
| internal_emigration | 0.0028 | 0.082 | 0.0030 | 0.049 | ||||
| external_emigration | -0.0096 | 0.123 | -0.0123 | 0.044 | 0.1110 | <0.001 | 0.1402 | <0.001 |
| internal_immigration | -0.0025 | 0.039 | -0.0021 | 0.085 | ||||
| empl_agriculture | -0.0003 | 0.108 | -0.0004 | 0.032 | ||||
| size_COVID | -0.0145 | 0.115 | ||||||
| mob_in | 0.0002 | 0.061 | ||||||
| mob_out | 0.0006 | 0.027 | 0.0004 | 0.119 | 0.0032 | 0.003 | 0.0042 | <0.001 |
| log_mobility | -8.5689 | <0.001 | -7.6686 | <0.001 | ||||
| closeness_mob | -258546.5005 | 0.088 | ||||||
| industry_revenue | -0.0028 | 0.030 | -0.0030 | 0.018 | ||||
| empl_services | 0.0018 | <0.001 | 0.0012 | <0.001 | ||||
| poulation_density | -0.0179 | 0.028 | ||||||
| arrival_SARS | -0.4407 | 0.081 | ||||||
| pagerank_mob | -2439.3479 | 0.130 | ||||||
| neighbors_size | 0.0006 | <0.001 | ||||||
| PM | 1.9359 | <0.001 | 2.7087 | <0.001 | ||||
| industry_fraction | 0.0001 | 0.064 | 0.0001 | 0.023 | ||||
| Observations | 380 | 370 | 380 | 370 | ||||
| R2 / R2 adjusted | 0.284 / 0.260 | 0.273 / 0.255 | 0.529 / 0.516 | 0.514 / 0.505 | ||||
| AIC | 3060.042 | 2986.258 | 4320.816 | 4209.976 | ||||
mod_all=tab_model(step.model,step.model2, step.model_s,step.model_s2, digits = 3, show.ci = FALSE, show.aic=TRUE)
mod_all
| Â | arrival_SARS | arrival_SARS | size_COVID | size_COVID | ||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | p | Estimates | p | Estimates | p | Estimates | p |
| (Intercept) | 97.425 | <0.001 | 103.472 | <0.001 | -57.322 | 0.005 | -99.045 | <0.001 |
| forest_density | 7.830 | 0.144 | ||||||
| longitude | 0.559 | 0.151 | 0.650 | 0.096 | ||||
| PIS_support | -0.184 | 0.052 | -0.204 | 0.033 | ||||
| postproduction_age | -0.000 | 0.095 | ||||||
| internal_emigration | 0.003 | 0.082 | 0.003 | 0.049 | ||||
| external_emigration | -0.010 | 0.123 | -0.012 | 0.044 | 0.111 | <0.001 | 0.140 | <0.001 |
| internal_immigration | -0.002 | 0.039 | -0.002 | 0.085 | ||||
| empl_agriculture | -0.000 | 0.108 | -0.000 | 0.032 | ||||
| size_COVID | -0.014 | 0.115 | ||||||
| mob_in | 0.000 | 0.061 | ||||||
| mob_out | 0.001 | 0.027 | 0.000 | 0.119 | 0.003 | 0.003 | 0.004 | <0.001 |
| log_mobility | -8.569 | <0.001 | -7.669 | <0.001 | ||||
| closeness_mob | -258546.501 | 0.088 | ||||||
| industry_revenue | -0.003 | 0.030 | -0.003 | 0.018 | ||||
| empl_services | 0.002 | <0.001 | 0.001 | <0.001 | ||||
| poulation_density | -0.018 | 0.028 | ||||||
| arrival_SARS | -0.441 | 0.081 | ||||||
| pagerank_mob | -2439.348 | 0.130 | ||||||
| neighbors_size | 0.001 | <0.001 | ||||||
| PM | 1.936 | <0.001 | 2.709 | <0.001 | ||||
| industry_fraction | 0.000 | 0.064 | 0.000 | 0.023 | ||||
| Observations | 380 | 370 | 380 | 370 | ||||
| R2 / R2 adjusted | 0.284 / 0.260 | 0.273 / 0.255 | 0.529 / 0.516 | 0.514 / 0.505 | ||||
| AIC | 3060.042 | 2986.258 | 4320.816 | 4209.976 | ||||
p<-ggplot(all_election_2, aes(x="",y=arrival_SARS-1)) +theme_bw()+
ylab("days since first case on 04.03") + xlab("density of No. poviats") +
geom_violin(fill="green")
p
p<-ggplot(all_election_2, aes(x="",y=size_COVID)) +theme_bw()+
ylab("No. cases")+xlab("density of No. poviats")+
geom_violin(fill="red")
p
moran’s spatial correlations
library(ape)
## Warning: package 'ape' was built under R version 3.6.3
dists.inv <- 1/macierz
diag(dists.inv ) <- 0
Moran.I(all_election_2$arrival_SARS, dists.inv)
## $observed
## [1] 0.02380674
##
## $expected
## [1] -0.002638522
##
## $sd
## [1] 0.005191331
##
## $p.value
## [1] 3.503653e-07
Moran.I(all_election_2$size_COVID, dists.inv)
## $observed
## [1] 0.08381928
##
## $expected
## [1] -0.002638522
##
## $sd
## [1] 0.005023173
##
## $p.value
## [1] 0
all_election=all_election_2
all_election$pred_arr<-mod_arr$fitted.values
all_election$pred_size<-mod_size$fitted.values
all_election$res_size<-mod_size$residuals
#scaling to standard normal distribution
all_election$pred_arr_s<-scale(all_election$pred_arr)
all_election$pred_size_s<-scale(mod_size$fitted.values)
all_election$res_size_s<-scale(mod_size$residuals)
all_election$opt=NA
all_election$opt_bin=0
for (i in 1:380) {
all_election$opt[i]=as.numeric(all_election$res_size_s[i])+as.numeric(all_election$pred_size_s[i])-as.numeric(all_election$pred_arr_s[i])
}
wybrane=which(all_election$opt>0)
sum_all=sum(all_election$opt[wybrane])
all_election$allocation=NA
for (i in wybrane){
all_election$allocation[i]=ceiling(all_election$opt[i]*1000/sum_all)
all_election$opt_bin[i]=1
}
all_election2$allocation=all_election$allocation
library(rgdal)
## Warning: package 'rgdal' was built under R version 3.6.2
## Loading required package: sp
## rgdal: version: 1.4-8, (SVN revision 845)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
## Path to GDAL shared files: C:/Users/gulak/OneDrive/Documents/R/win-library/3.6/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
## Path to PROJ.4 shared files: C:/Users/gulak/OneDrive/Documents/R/win-library/3.6/rgdal/proj
## Linking to sp version: 1.3-2
require(rgeos)
## Loading required package: rgeos
## Warning: package 'rgeos' was built under R version 3.6.2
## rgeos version: 0.5-2, (SVN revision 621)
## GEOS runtime version: 3.6.1-CAPI-1.10.1
## Linking to sp version: 1.3-2
## Polygon checking: TRUE
poland_powiaty_eqa <- readOGR(dsn="C://Users/gulak/Downloads/Nowy folder/asf/ComputationalEpidemiologyASF-master/ProjektML_HEX/Maps", "powiaty")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\gulak\Downloads\Nowy folder\asf\ComputationalEpidemiologyASF-master\ProjektML_HEX\Maps", layer: "powiaty"
## with 380 features
## It has 29 fields
poland_powiaty <- readOGR(dsn="C://Users/gulak/Downloads/Nowy folder/asf/ComputationalEpidemiologyASF-master/ProjektML_HEX/Maps", "powiaty")
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\gulak\Downloads\Nowy folder\asf\ComputationalEpidemiologyASF-master\ProjektML_HEX\Maps", layer: "powiaty"
## with 380 features
## It has 29 fields
poland_powiaty <- spTransform(poland_powiaty,
CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
poland_powiaty <- gSimplify(poland_powiaty, tol = 5000, topologyPreserve = TRUE)
poland_powiaty_eqa@data$id=c(0:379)
data1 = fortify(poland_powiaty) %>% left_join(.,poland_powiaty_eqa@data %>% mutate(id = as.character(id)), by = "id" )
#data1$jpt_kod_je2=as.character(paste(data1$jpt_kod_je))
for (i in 1:380){
if (all_election2$ter_map[i]<1000)
{all_election2$ter_0[i]=paste("0",as.character(all_election2$ter_map[i]), colapse="", sep="")} else
{all_election2$ter_0[i]=as.character(all_election2$ter_map[i])}
}
all_election2$jpt_kod_je=as.factor(all_election2$ter_0)
#all_election2$jpt_kod_je=as.character(paste(all_election2$c4))
dane= inner_join(all_election2, data1, by = c("jpt_kod_je"= "jpt_kod_je"))
#dag=simplify(dane)
pdf("alokacja_.pdf")
ggplot() +
theme_bw()+
geom_polygon(data = dane,
aes(long, lat, group = group, fill = allocation),
#fill = "grey50",
#alpha = 0,lwd=0.1,
colour = "white") +
# scale_color_manual(values = mycolors) +
scale_color_distiller("alocation", palette = "Spectral")+
ggtitle("Best allocaton of new 1000 Disease Intervention Specialists")
dev.off()
## png
## 2
diagnostics
pdf("mod_arr_cos2_.pdf")
par(mfrow = c(2,2))
par(ask=F)
plot(step.model2_, bty='l', cex.main = 1, cex.lab=1.5, cex.axis=2, cex = 1)
dev.off()
## png
## 2
pdf("mod_size_cos2_).pdf")
par(mfrow = c(2,2))
par(ask=F)
plot(step.model_s2_, bty='l', cex.main = 1, cex.lab=1.5, cex.axis=2, cex = 1)
dev.off()
## png
## 2
ncvTest(step.model_s)
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 188.2834, Df = 1, p = < 2.22e-16
# Q-Q plot
ggplot(step.model_s2_, aes(.fitted, .resid)) +
geom_hline(yintercept = 0) +
geom_point() +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(step.model_s2_, aes(sample = .stdresid)) + stat_qq() + theme(text = element_text(size = 20)) + geom_abline()
#Shapiro-Wilk's test: p < 0.05 indicates non-normality:
shapiro.test(resid(step.model_s2_))
##
## Shapiro-Wilk normality test
##
## data: resid(step.model_s2_)
## W = 0.7362, p-value < 2.2e-16
# collinearity
#row.names(arr)=all_election2$id.y
.rowNamesDF(arr2_, make.names=TRUE) <- all_election2$id.y
.rowNamesDF(size2_, make.names=TRUE) <- all_election2$id.y
vif(mod_arr2_)
## population_size forest_density longitude
## 2.425536 1.318123 2.110235
## PIS_support income postproduction_age
## 3.782306 1.631305 3.333989
## internal_emigration external_emigration internal_immigration
## 45.307621 2.116985 45.625802
## external_immigration industry_revenue empl_agriculture
## 22.246440 9.489893 2.767571
## empl_industry empl_services poulation_density
## 20.041885 91.972231 3.449822
## mob_in mob_out log_mobility
## 26.713089 6.294810 6.205280
## pagerank_mob betweenness_mob closeness_mob
## 31.840791 5.199595 1.667531
## industry_fraction PM
## 5.704358 1.527870
vif(mod_size2_)
## population_size forest_density longitude
## 2.425536 1.318123 2.110235
## PIS_support income postproduction_age
## 3.782306 1.631305 3.333989
## internal_emigration external_emigration internal_immigration
## 45.307621 2.116985 45.625802
## external_immigration industry_revenue empl_agriculture
## 22.246440 9.489893 2.767571
## empl_industry empl_services poulation_density
## 20.041885 91.972231 3.449822
## mob_in mob_out log_mobility
## 26.713089 6.294810 6.205280
## pagerank_mob betweenness_mob closeness_mob
## 31.840791 5.199595 1.667531
## industry_fraction PM
## 5.704358 1.527870
sqrt(vif(step.model_s2_)) > 2
## income external_emigration industry_revenue
## FALSE FALSE TRUE
## empl_services mob_out betweenness_mob
## TRUE FALSE FALSE
## PM
## FALSE
# outlier test
outlierTest(step.model_s2_)
## rstudent unadjusted p-value Bonferroni p
## 141 8.944814 1.7973e-17 6.8297e-15
## 226 5.992239 4.9017e-09 1.8626e-06
## 164 4.598794 5.8383e-06 2.2185e-03
## 224 4.275937 2.4238e-05 9.2104e-03
## 45 4.121054 4.6553e-05 1.7690e-02
pdf("influence_arr_.pdf")
influencePlot(step.model2_, id.method="identify", main="Influence Plot for arrival time",
sub="Circle size is proportional to Cook's distance")
## Warning in plot.window(...): "id.method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "id.method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is
## not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is
## not a graphical parameter
## Warning in box(...): "id.method" is not a graphical parameter
## Warning in title(...): "id.method" is not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "id.method" is not a
## graphical parameter
dev.off()
## png
## 2
pdf("influence_size2.pdf")
influencePlot(step.model_s2_, id.method="identify", main="Influence Plot for No. cases",
sub="Circle size is proportional to Cook's distance")
## Warning in plot.window(...): "id.method" is not a graphical parameter
## Warning in plot.xy(xy, type, ...): "id.method" is not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is
## not a graphical parameter
## Warning in axis(side = side, at = at, labels = labels, ...): "id.method" is
## not a graphical parameter
## Warning in box(...): "id.method" is not a graphical parameter
## Warning in title(...): "id.method" is not a graphical parameter
## Warning in plot.xy(xy.coords(x, y), type = type, ...): "id.method" is not a
## graphical parameter
dev.off()
## png
## 2
fig <- plot_ly(all_election, x = ~pred_arr, y = ~res_size, z = ~pred_size, color = ~opt_bin, colors = c('#BF382A', '#0C4B8E'))
fig <- fig %>% add_markers()
fig <- fig %>% layout(scene = list(xaxis = list(title = 'Predicted arriva time'),
yaxis = list(title = 'Residuals No. cases'),
zaxis = list(title = 'Predicted No. cases')))
fig